Displacement Model for Concrete Dam Safety Monitoring via Gaussian Process Regression Considering Extreme Air Temperature

AbstractStructural health monitoring models provide important information for safety control of large dams. The main challenge in developing an accurate dam behavior prediction model lies in the mo...

[1]  J.M.G. Sá da Costa,et al.  Time–frequency analysis for concrete dam safety control: Correlation between the daily variation of structural response and air temperature , 2013 .

[2]  Dieu Tien Bui,et al.  A novel hybrid artificial intelligent approach based on neural fuzzy inference model and particle swarm optimization for horizontal displacement modeling of hydropower dam , 2018, Neural Computing and Applications.

[3]  Donghoon Shin,et al.  Development of dam safety management system , 2009, Adv. Eng. Softw..

[4]  José Sá da Costa,et al.  Constructing statistical models for arch dam deformation , 2014 .

[5]  Nenad Grujovic,et al.  Development of support vector regression identification model for prediction of dam structural behaviour , 2014 .

[6]  Chin-Hsiung Loh,et al.  Monitoring of long‐term static deformation data of Fei‐Tsui arch dam using artificial neural network‐based approaches , 2013 .

[7]  Eugenio Oñate,et al.  An empirical comparison of machine learning techniques for dam behaviour modelling , 2015 .

[8]  Eugenio Oñate,et al.  Data-Based Models for the Prediction of Dam Behaviour: A Review and Some Methodological Considerations , 2017 .

[9]  J. Mata,et al.  Interpretation of concrete dam behaviour with artificial neural network and multiple linear regression models , 2011 .

[10]  Su Huaizhi,et al.  Nonlinear combined monitoring of dam safety , 2005 .

[11]  Mahesh Pal,et al.  Modelling pile capacity using Gaussian process regression , 2010 .

[12]  Lin Cheng,et al.  Two online dam safety monitoring models based on the process of extracting environmental effect , 2013, Adv. Eng. Softw..

[13]  James-A. Goulet,et al.  Structural Health Monitoring with dependence on non-harmonic periodic hidden covariates , 2018, Engineering Structures.

[14]  Sooyong Choi,et al.  Blind equalizer for constant-modulus signals based on Gaussian process regression , 2012, Signal Process..

[15]  Junjie Li,et al.  Structural inverse analysis by hybrid simplex artificial bee colony algorithms , 2009 .

[16]  Michael Oberguggenberger,et al.  Assessment of long‐term coordinate time series using hydrostatic‐season‐time model for rock‐fill embankment dam , 2017 .

[17]  Jia Liu,et al.  Concrete dam deformation prediction model for health monitoring based on extreme learning machine , 2017 .

[18]  Nikola Milivojevic,et al.  A self-tuning system for dam behavior modeling based on evolving artificial neural networks , 2016, Adv. Eng. Softw..

[19]  Bin Xu,et al.  Slope stability evaluation using Gaussian processes with various covariance functions , 2017, Appl. Soft Comput..

[20]  Giulio Maier,et al.  Diagnostic analysis of concrete dams based on seasonal hydrostatic loading , 2008 .

[21]  Chin-Hsiung Loh,et al.  Application of advanced statistical methods for extracting long-term trends in static monitoring data from an arch dam , 2011 .

[22]  Matthieu Briffaut,et al.  Statistical modelling of thermal displacements for concrete dams: Influence of water temperature profile and dam thickness profile , 2018, Engineering Structures.

[23]  Junjie Li,et al.  System probabilistic stability analysis of soil slopes using Gaussian process regression with Latin hypercube sampling , 2015 .

[24]  F. Dufour,et al.  Thermal displacements of concrete dams: Accounting for water temperature in statistical models , 2015 .

[25]  A. De Sortis,et al.  Statistical analysis and structural identification in concrete dam monitoring , 2007 .

[26]  Wei Xiong,et al.  Modeling method for predicting seepage of RCC dams considering time‐varying and lag effect , 2018 .

[27]  Pilate Moyo,et al.  Health monitoring of concrete dams: a literature review , 2014 .

[28]  Meng Yang,et al.  Time-varying identification model for dam behavior considering structural reinforcement , 2015 .

[29]  Nenad Grujovic,et al.  Modelling of dam behaviour based on neuro-fuzzy identification , 2012 .

[30]  Jinping He,et al.  A statistical model of deformation during the construction of a concrete face rockfill dam , 2018 .

[31]  Eugenio Oñate,et al.  Early detection of anomalies in dam performance: A methodology based on boosted regression trees , 2017 .

[32]  Carl E. Rasmussen,et al.  Gaussian Processes for Machine Learning (GPML) Toolbox , 2010, J. Mach. Learn. Res..

[33]  Fei Kang,et al.  Structural health monitoring of concrete dams using long-term air temperature for thermal effect simulation , 2019, Engineering Structures.

[34]  Guohua Liu,et al.  Hydrostatic seasonal state model for monitoring data analysis of concrete dams , 2015 .

[35]  Guohua Liu,et al.  Towards an Error Correction Model for dam monitoring data analysis based on Cointegration Theory , 2013 .

[36]  Yu Hu,et al.  Safety Monitoring of High Arch Dams in Initial Operation Period Using Vector Error Correction Model , 2018, Rock Mechanics and Rock Engineering.

[37]  Nikola Milivojevic,et al.  Adaptive system for dam behavior modeling based on linear regression and genetic algorithms , 2013, Adv. Eng. Softw..

[38]  Junjie Li,et al.  Prediction of long-term temperature effect in structural health monitoring of concrete dams using support vector machines with Jaya optimizer and salp swarm algorithms , 2019, Adv. Eng. Softw..

[39]  Guang-yong Xi,et al.  Application of an artificial immune algorithm on a statistical model of dam displacement , 2011, Comput. Math. Appl..

[40]  Bowen Wei,et al.  Combination forecast model for concrete dam displacement considering residual correction , 2019 .

[41]  Sriram Narasimhan,et al.  Initial service life data towards structural health monitoring of a concrete arch dam , 2018 .